Expired50% confidenceFactExact time
In non-deterministic LLM systems, even when using the same prompt and parameters, outputs can differ due to the model's internal sampling mechanisms (temperature, top-p, etc.), API version updates, or floating-point arithmetic precision differences.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Related Claims
UnverifiedThe temperature parameter design in the language model sampling process means that the same prompt may produce completely different outputs across different calls, so retrying itself does not guarantee convergence.77% similarUnverifiedLLM 的输出非确定性根源于自回归生成机制，即使输入相同，Temperature、Top-p 等采样参数也会导致不同输出77% similarVerifiedLLM的不一致性与模型的温度参数和随机采样过程密切相关，同一问题多次调用同一模型可能产生不同答案77% similarVerifiedThe output of LLMs is inherently non-deterministic; even with the same Prompt as input, the model may return results with different formats, content, or even logic at different times.76% similarUnverifiedLLM网关面临泄漏抽象问题，模型能力的本质差异会泄漏到上层，如system prompt格式、temperature参数语义、function calling调用规范差异76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/50176API
curl https://kongchang.com/api/v1/knowledge/claims/50176MCP
get_claim(id=50176)